1. Field of the Invention
The present invention relates to a learning apparatus and method of an artificial neural network.
2. Description of the Background
Conventionally, a teacher-supervised learning method of a neural network is carried out as follows. For learning a fact that one input data should be assigned to a class presently being observed, learning is carried out only when the input data is not assigned to the class being observed. In other words, when the input data shows the largest output value for the class being observed, learning will be carried out. Such a method is disclosed, for example, in a report "Statistical Pattern Recognition with Neural Networks: Benchmarking Studies" by T. Kohonen, G. Barna and R. Chrisley in IEEE, Proc. of ICNN, Vol I, pp 61-68, July 1988.
As described above, according to the learning method of the neural network, when a learning data is inputted, learning is carried out only when the output node, which is not assigned to the class in which the learning data should belong, produces a largest output value. In other words, with respect to all the learning data, when the output node assigned to the class to which the learning data belongs produces the largest output, learning is ended. After learning, the neural network can classify a data which is unlearned, but similar to the earned data, into a proper class. Therefore, according to the conventional art learning apparatus or method, the neural network can provide a high recognition ability for unlearned data, provided that a variety of sufficient patterns of learning data are used for learning one class.
However, when very few learning data are used, or when a large amount of learning data with less variation in pattern change are used for learning one class, the neural network can not provide a high recognition ability after learning.